infographics and visualisation (or: beyond the pie chart · • data analysis may tell you...

Infographics and Visualisation

(or: Beyond the Pie Chart)

LSS: ITNPBD4, 6 and 10 Oct 2015

ITNPD4: Applications of Big Data 2

Overview –  Why infographics and visualisation

–  What’s the problem we’re trying to solve? –  What makes for good infographics and visualisations? –  Where are we now in this area?

Overview

The problem

•  Data analysis may tell you something about the structure of a problem

•  Or may predict how to optimise something –  Profit, energy usage etc.

•  BUT: –  In general you will have to convince someone –  And they may not be convinced by the numbers on their

own •  They expect some sort of graphic that they can

show to the Board/CEO to convince them –  A visualisation, perhaps an infographic.


Visualisation and infographics

•  Visualisation is the generic name for displaying data –  May be a single image –  Or a movie, for example. “Visualizations help people see things that were not obvious to them before” (SAS website)

•  There is also sonification, where data is sounded out: this works, because our ears are very good a picking up patterns. –  E.g. Geiger counter, reversing systems in modern cars.

•  Infographics are generally single images –  Providing a visualisation of a specific set of data.


Infographics •  An infographic is a

picture that displays information in an accessable and/or informative way.

•  Can be quite simple •  …or quite complex


…not a new idea (Minard, 1869)!


The standard text in this area is E. R. Tufte, “The visual display of quantitative information”


Infographic shows the troops and troop movements on the eastern from in World War 2.

Visualisation of low-dimensional datasets

•  Low-dimensional datasets are often visualised as simple X/Y graphs: but even here there are issues –  For both X and Y axes:

•  Offset (is the origin at 0?) •  Scale •  Linear or logarithmic? •  Continuous or broken axes.

–  Graph lines: •  One or more than one? •  Line style: continuous, dashed, dotted… •  Line colour •  Symbols and/or lines?



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Visualising high dimensional datasets

•  This is harder: and can be where infographics comes in –  Cannot do this directly.

•  Can plot two or three dimensions directly, but not more •  Clever infographics can plot more dimensions, for example using

geographical location, lines of varying thickness and colour, multiple symbols

–  How can we show the structure of such datasets? •  When we can’t think of one-off target-domain clever tricks …

–  Discuss earlier infographics

•  Clearly depends on what we are trying to show! –  Geography as timeline, for example –  See also http://www.creativebloq.com/graphic-design-tips/great-

infographic-design-tips-1232813


What can we do in general

•  Let’s say that we don’t have any inspiration for designing a good infographic (!) –  Infographics often depends on specific factors

•  E.g. dates, geographic distribution, …

•  Can we find 2 or 3 (or even a few more) dimensions that … – … in some sense

•  …summarise (what we want to emphasise about) the dataset?

•  Ways forward: projecting and clustering


Choosing dimensions and projecting data

•  If the data is evenly spread throughout all the dimensions and has no structure? –  Give up. There’s nothing to be learned from it.

•  Datasets that have something to tell us have some from of structure

•  Maybe the data lie (largely) on a smaller dimensional subset of the high-dimensional space. –  As opposed to being spread evenly throughout the

original space.


Example •  Say that we have 3-dimensional data, sampled over time

–  Each point is (x,y,z,t): really 4-dimensional data •  and -1 <= x,y,z <=1, 0<=t<=10 (the points (x,y,z) are

inside a sphere, of radius 1, centered at the origin) •  Let’s also say that at each time t, sqrt(x2+y2+z2) = t/10

–  So that the points at time t are on the surface of a sphere of radius t/10

•  Clearly, if we simply look at all the(x,y,z) points (ignoring t) they are spread throughout the sphere –  But not in an unstructured way


Discovering structure in data

•  There are many techniques for discovering structure –  Principal component analysis (pca)

•  Linearly projecting a high dimensional dataset on to a smaller number of dimensions

•  In such a way that as much as possible of the variance in the data is contained in this smaller number of dimensions

•  And the dimensions are orthogonal to each other •  Well-understood and commonly used technique for data

dimension reduction


Independent components analysis •  Independent components analysis (ica)

–  a statistical and computational technique for revealing hidden factors that underlie sets of random variables, measurements, or signals. Hyvärinen, (U Helsinki)

•  Essentially looking for dimensions that co-vary •  Finding ways of summarising points in the N-dimensional

space using less than N values. •  Data is assumed to be a linear mixture of underlying

latent variables –  These are assumed non-Gaussian, and mutually independent:

independent components •  Related to PCA, but can find structure when PCA fails to

do so


Example: input


ICA output


Clustering data

•  Often rather than projecting data on to other axes, it is better to look at how the data points are grouped –  The aim is to classify a large number of data vectors

into a small number of manageable groups •  Does the data fall into clusters?

–  How unevenly distributed is the data? –  Does it cluster in

•  The original high-dimensional space •  In a lower-dimensional projected space?


How does clustering work?

•  Techniques –  Partition or Hierarchical


Examples


Partition-based clustering •  Based on distance between vectors

–  But which distance? •  Euclidean •  City-block? •  Weighted versions •  Chebychev distance

•  Forming clusters: –  Simple method:

•  Start with each vector as a single-element cluster •  Identify two closest vectors and combine them into the same

cluster. •  Keep doing this until the distance between the two closest

vectors not in the same cluster is large.


Criticisms of clustering

•  Clustering is descriptive, and not unique –  Actual clusters may depend on techniques used, as well

as on the data •  Clustering techniques will always find clusters

–  Even when there aren’t any! –  (This implies some measure for qualirty of clustering

should be used) •  Clustering techniques depend strongly on the

measures used –  There should ideally be some conceptual support of the

measures used to calculate distances between vectors.


Examples:

•  Google News indexes –  Uses text to create topic clusters

•  Title, article listings •  Used to discover multiple reports of same story

•  Video clusters on YouTube –  Uses keywords, popularity, viewer engagement, user

browsing history –  http://www.strutta.com/blog/six-degrees-of-youtube/


Infographics tools

•  At its simplest, Excel has many facilities for creating infographics and visualisations. –  But it’s limited, and proprietary (though one can

import comma separated values) •  Matlab? Not free! Good graphing tools •  Flot: jQuery and JavaScript based •  Google Chart API: free

–  JavaScript based, browser output

•  D3: JavaScript based, very powerful.


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